Background

Malaria, a life-threatening disease transmitted by mosquitoes and caused by Plasmodium parasites, presents a significant global health challenge. In 2022, it accounted for an estimated 249 million cases worldwide, resulting in approximately 608,000 deaths. Within Southeast Asia, including Malaysia, malaria remains a persistent public health concern, historically attributed to Plasmodium falciparum and vivax parasites. Despite successful national efforts to eliminate indigenous malaria cases in Malaysia since 2018, the emergence of Plasmodium knowlesi (Pk), previously confined to macaque monkeys but now infecting humans, has raised new alarms.

Cerebral malaria is one of the most severe forms of malaria infection, affecting the brain and often causing coma. Survivors can suffer from brain problems even after the infection is treated. While cerebral malaria is usually linked to Plasmodium falciparum, there have been cases where other types of Plasmodium parasites also caused severe brain issues. Recent research has shown that Pk infections in Southeast Asia can also lead to severe complications, sometimes even resulting in death. Post-mortem examinations in one fatal case of severe Pk infection revealed brain damage similar to that seen in cerebral malaria caused by Plasmodium falciparum, even though Pk parasites don’t usually bind to brain blood vessels like Plasmodium falciparum does.

Our own research has found brain changes in Indian patients with severe malaria, even if they didn’t have coma. We observed higher levels of a protein associated with brain damage in these patients (S100B), along with kidney problems. This suggests that malaria can impact the brain, regardless of whether coma occurs, and kidney issues may worsen the situation. Despite similarities between cerebral malaria and severe Pk infections, and the frequent occurrence of kidney problems in severe Pk malaria, we still don’t know much about how Pk infection affects the human brain. To address this gap, we studied biomarkers related to brain injury, blood vessel activity, and inflammation in Malaysian patients infected with Pk and compared them to healthy individuals.

Methodology

Participants and samples

This study utilized stored plasma samples collected during a larger project conducted in Peninsular Malaysia from December 2019 to January 2023. Adult patients exhibiting symptoms of malaria and seeking treatment at government hospitals or private clinics across several states in Malaysia were invited to participate. Additionally, community-matched, age-matched individuals without malaria were recruited from various communities in Malaysia. These individuals were considered part of high-risk groups, such as those working in or near forests, including farmers, hunters, and natural resource collectors.

Blood samples were collected after participants provided informed consent. All data were anonymised to protect participants’ privacy. Malaria infection in each sample was confirmed using microscopic examination and molecular techniques. Out of the 50 individuals initially sampled, 38 had relevant clinical information available for this study, including parasitaemia levels, which our group has previously identified as an age-independent factor associated with severity in falciparum malaria. For this study, we analyzed de-identified samples from 19 infected patients and 19 healthy controls from Malaysia.

This study was reviewed and approved by the Medical Research and Ethics Committee of the Ministry of Health in Malaysia (22-02557-1KV) and by the Observational/Interventions Research Ethics Committee at the London School of Hygiene and Tropical Medicine, UK (27902). The samples are registered with the Human Tissue Authority, in accordance with UK national guidelines.

Statistical analysis

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Load required packages

  library(data.table)
  library(plyr)
  library(tidyverse)
  library(readr)
  library(readxl)
  library(haven)
  library(ggplot2)
  library(lavaan)
  library(cowplot)
  library(rmarkdown)
  library(gtsummary)
  library(pheatmap)
  library(ggpubr)
  library(knitr)
  library(kableExtra)
  library(plotly)
  library(remotes)
  library(leaflet)
  library(sf)
  library(webshot2)
  library(htmlwidgets)
  library(Hmisc)

Prepare datasets

# Read Data

  Pk_Malaysia_database <- read_excel("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/Pk_Malaysia_database.xlsx")
  Pk_Malaysia_database[, (4:5)] <- lapply(Pk_Malaysia_database[, (4:5)], as.factor)
  Pk_Malaysia_database[, (7:8)] <- lapply(Pk_Malaysia_database[, (7:8)], as.factor)
  
  Pk_Malaysia_Luminex <- read_excel("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/Pk_Malaysia_Luminex.xlsx")
  Pk_Malaysia_Luminex$Dilution <- factor(Pk_Malaysia_Luminex$Dilution)
  Pk_Malaysia_Luminex <- Pk_Malaysia_Luminex[,-57]
  
  Pk_Malaysia_S100B <- read_excel("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/Pk_Malaysia_S100B.xlsx")
  
  Pk_Malaysia_Simoa <- read_excel("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/Pk_Malaysia_Simoa.xlsx")
  
# Merge based on sample_ID
  
  Pk_Malaysia_1in2 <- Pk_Malaysia_Luminex %>% filter(Dilution == "1in2") # Removing biomarkers at 1:50 dilution
  Pk_Malaysia_1in2 <- Pk_Malaysia_1in2[,!names(Pk_Malaysia_1in2) %in% c("CCL18", "CCL5", "CRP", "Fetuin A", "Lipocalin 2", 
                                                                        "MPO", "PDGF BB", "Serpin E1", "Serpin F1", "NSE", "IL-1RA")] 
  Pk_Malaysia_1in2$`Ang-2/Ang-1 ratio` <- Pk_Malaysia_1in2$`Ang-2`/Pk_Malaysia_1in2$`Ang-1`
  
  Pk_Malaysia_1in50 <- Pk_Malaysia_Luminex %>% filter(Dilution == "1in50") # Removing biomarkers at 1:2 dilution
  Pk_Malaysia_1in50 <- Pk_Malaysia_1in50[,names(Pk_Malaysia_1in50) %in% c("sample_ID", "CCL18", "CCL5", "CRP", "Fetuin A", 
                                                                          "Lipocalin 2", "MPO", "PDGF BB", "Serpin E1", "Serpin F1", 
                                                                          "NSE", "IL-1RA")] 
  
  Pk_Malaysia <- merge(x = Pk_Malaysia_1in2, y = Pk_Malaysia_1in50, by = "sample_ID", all.x = TRUE)
  Pk_Malaysia <- Pk_Malaysia[,-c(2:5)] # Remove unnecessary Luminex info
  Pk_Malaysia <- Pk_Malaysia[,!names(Pk_Malaysia) %in% c("GFAP", "UCHL-1", "Tau total")] # Remove biomarkers re-analysied with SIMOA
  
  Pk_Malaysia <- merge(x = Pk_Malaysia, y = Pk_Malaysia_database, by = "sample_ID", all.x = TRUE)
  Pk_Malaysia <- merge(x = Pk_Malaysia, y = Pk_Malaysia_S100B, by = "sample_ID", all.x = TRUE)
  Pk_Malaysia <- merge(x = Pk_Malaysia, y = Pk_Malaysia_Simoa, by = "sample_ID", all.x = TRUE)
  
  Pk_Malaysia <- Pk_Malaysia %>% relocate(c(2:55), .after = last_col())
  
  Pk_Malaysia_final <- Pk_Malaysia[,!names(Pk_Malaysia) %in% c("CNTF", "FGF-21", "GDNF", "NfH", "Tau ptT81")] # Remove undetectable biomarkers
  Pk_Malaysia_final$S100B[is.na(Pk_Malaysia_final$S100B)] <- 27.3045 # Follow statistician's advice on S100B: Values below range (<54.6091) are replaced by half the threshold value 
  Pk_Malaysia_final$`Aβ(1-42)`[is.na(Pk_Malaysia_final$`Aβ(1-42)`)] <- 0.2197 # Follow statistician's advice on Aβ: Values below range (<0.4395) are replaced by half the threshold value
  Pk_Malaysia_final <- Pk_Malaysia_final[,!names(Pk_Malaysia_final) %in% c("Serpin F1")] # Remove biomarkers w/o values in one of the groups
  
  for (i in 1:length(Pk_Malaysia_final$parasitaemia)) { # Transform parasitaemia from % to parasites/uL
  if (!is.na(Pk_Malaysia_final$parasitaemia[i])) {
    Pk_Malaysia_final$parasitaemia[i] <- (Pk_Malaysia_final$parasitaemia[i]/100) * 5000000
  }
}
  
# Reshape dataset for figures
  
  Pk_Malaysia_plots <- Pk_Malaysia_final %>% gather(Biomarker, Levels, 11:63)

Sample collection sites

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You can hover over the map with your cursor and click the different items to visualize hospital names.

Pk_Malaysia_provinces <- st_read("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/gadm41_MYS_shp/gadm41_MYS_1.shp")
## Reading layer `gadm41_MYS_1' from data source 
##   `/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/gadm41_MYS_shp/gadm41_MYS_1.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 16 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 99.64072 ymin: 0.85372 xmax: 119.2697 ymax: 7.380556
## Geodetic CRS:  WGS 84
Pk_Malaysia_provinces_CTRL <- Pk_Malaysia_provinces %>%
  filter(NAME_1 %in% c("Johor", "Selangor", "Negeri Sembilan", "Kedah"))

Pk_Malaysia_map <- leaflet(data) %>% # Create a leaflet map 
  addTiles() %>%
  setView(lng = 101.9758, lat = 4.2105, zoom = 6)  # Centered on Malaysia

Pk_Malaysia_map <- Pk_Malaysia_map %>% # Highlight provinces where controls were recruited
  addPolygons(
    data = Pk_Malaysia_provinces_CTRL,
    fillColor = "yellow",  
    fillOpacity = 0.5, 
    stroke = FALSE,
    popup = ~NAME_1
  )

Pk_Malaysia_map_Pk_infected <- Pk_Malaysia_final %>% # Now filter out healthy control rows 
  filter(!is.na(latitude) & !is.na(longitude) & !is.na(NAME_1) & !is.na(hospital))

samples_per_hospital <- Pk_Malaysia_final %>% # This is to add number of samples collected per hospital
  count(hospital) %>%
  rename(samples_collected = n)
Pk_Malaysia_map_Pk_infected <- left_join(Pk_Malaysia_map_Pk_infected, samples_per_hospital, by = "hospital")

Pk_Malaysia_map <- Pk_Malaysia_map %>% # Add markers for hospitals with infected patients
  addCircleMarkers(
    data = Pk_Malaysia_map_Pk_infected,
    lng = ~longitude,
    lat = ~latitude,
    color = "red",
    radius = 5,
    popup = ~paste("Hospital:", hospital, "<br>Province:", NAME_1, "<br>Samples collected:", samples_collected)
  )

Pk_Malaysia_map

Pk-infected patients attending either a government hospital or private clinic in Johor, Selangor, Pahang, Perak, and Trengganu states were invited to participate. Recruitment of community-matched, age-matched uninfected controls was conducted via active sample screening of individuals from communities in Johor, Selangor, Negeri Sembilan, and Kedah. Map was created in R with RStudio via Leaflet and OpenStreetMap.

Data normality

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Pk_Malaysia_histograms <- ggplot(Pk_Malaysia_plots, aes(x=Levels, fill = status)) +
  geom_histogram(alpha = 0.5) +
  theme_classic() + 
  ggtitle("Histograms: Biomarker levels") + 
  facet_wrap(~Biomarker, scales = "free") + ylab("Count") +
  scale_fill_manual(values=c("black", "red"), name = "Status") 

Pk_Malaysia_histograms
Pk_Malaysia_density <- ggplot(Pk_Malaysia_plots, aes(x=Levels, fill = status)) + 
  geom_density(aes(y=1.1*after_stat(count)), na.rm = TRUE, alpha = 0.75) +
  theme_classic() + 
  ggtitle("Density plots: Biomarker levels") + 
  facet_wrap(~Biomarker, scales = "free") + ylab("Count") +
  scale_fill_manual(values=c("black", "red"), name = "Status")   

Pk_Malaysia_density 
Histograms (right click to open full-size image in another tab/window)
Histograms (right click to open full-size image in another tab/window)

Upon examination of histograms representing biomarker levels, it was evident that the distribution of most biomarkers deviates from normality. Given this, non-parametric statistical tests will be employed for group comparisons. By adopting this approach, we aimed to accurately assess differences in biomarker levels across teh two groups while minimizing the impact of non-normality on our analyses.

Density plots (right click to open full-size image in another tab/window)
Density plots (right click to open full-size image in another tab/window)

Similarly, upon inspection of density plots illustrating biomarker levels, it became apparent that the distribution of most biomarkers does not adhere to a normal distribution pattern.

Simple analysis of biomarker levels

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Pk_Malaysia_summary <- Pk_Malaysia_plots %>%
  group_by(Biomarker, status) %>%
  summarise(
    Participants = sum(!is.na(Levels)),  
    Median = format(median(Levels, na.rm = TRUE), scientific = FALSE),  
    IQR = format(IQR(Levels, na.rm = TRUE), scientific = FALSE)  
  )

Pk_Malaysia_summary_wide <- Pk_Malaysia_summary %>%
  pivot_wider(names_from = status, values_from = c(Participants, Median, IQR)) 

names(Pk_Malaysia_summary_wide) <- c("Biomarker", 
                                      "n CTRL", "n Pk", 
                                      "Median_Control", "Median_Pk_infection", 
                                      "IQR_Control", "IQR_Pk_infection")

Pk_Malaysia_summary_wide$Median_Control <- paste(Pk_Malaysia_summary_wide$Median_Control, 
                                                 "(", Pk_Malaysia_summary_wide$IQR_Control, ")", sep = " ")
Pk_Malaysia_summary_wide$Median_Pk_infection <- paste(Pk_Malaysia_summary_wide$Median_Pk_infection, 
                                                      "(", Pk_Malaysia_summary_wide$IQR_Pk_infection, ")", sep = " ")

Pk_Malaysia_summary_wide <- Pk_Malaysia_summary_wide[, -c(6, 7)]

names(Pk_Malaysia_summary_wide)[names(Pk_Malaysia_summary_wide) == "Median_Control"] <- "Median (IQR) CTRL"
names(Pk_Malaysia_summary_wide)[names(Pk_Malaysia_summary_wide) == "Median_Pk_infection"] <- "Median (IQR) Pk "

Brain injury biomarkers

Bonferroni-corrected analyses revealed notable differences in plasma biomarker levels between Pk-infected individuals and uninfected controls. Specifically, the Pk-infected group exhibited significantly elevated levels of brain injury biomarkers such as Tau, UCH-L1, αSyn, Park7, NRGN, and TDP-43, whereas levels of CaBD, CNTN1, NCAM-1, BDNF, GFAP, and KLK6 were significantly lower in the Pk-infected group.

Plasma levels of S100B and Aβ(1-42) were mostly undetectable in uninfected subjects, whereas the Pk-infected group showed significantly higher proportions of detectable levels for both biomarkers. Adjustments were made for individuals with undetectable levels, revealing significantly higher plasma S100B levels in the Pk-infected group after correction for multiple comparisons.

Immune activation biomarkers

Regarding biomarkers of infection and immune activation, levels of anti-inflammatory cytokines IL-1RA and IL-10, and pro-inflammatory cytokine MPO, were significantly higher in the Pk-infected group, while chemokines CCL4 and CCL5, pro-inflammatory cytokine CRP, and RAGE levels were significantly lower.

Vascular biomarkers

Endothelial activation biomarkers such as Ang-2/Ang-1 ratio and VCAM-1 levels were significantly elevated in Pk-infected patients, while Ang-1, BMP-9, PDGF-AA and -BB, and Serpin E1 levels were significantly lower.

Pk_Malaysia_final_renamed <- Pk_Malaysia_final
names(Pk_Malaysia_final_renamed) <- make.names(names(Pk_Malaysia_final_renamed)) # Ooopsies had to correct variable names with symbols such as "-" or else cannot use the function below

biomarkers <- names(Pk_Malaysia_final_renamed)[11:63]

Pk_Malaysia_Bonferroni <- purrr::map_dfr(biomarkers, function(x) {
  f <- as.formula(paste0("`", x, "`", '~ status'))  # Use backticks to handle column names with spaces or symbols
  result <- wilcox.test(formula = f, data = Pk_Malaysia_final_renamed, exact = TRUE)
  return(data.frame(name = x, p.value = result$p.value))
})

Pk_Malaysia_Bonferroni$p.adj <- p.adjust(Pk_Malaysia_Bonferroni$p,method="bonferroni")

colnames(Pk_Malaysia_Bonferroni)[2:3] <- c("P value", "Corrected P value")

Pk_Malaysia_summary_wide$Biomarker <- make.names(Pk_Malaysia_summary_wide$Biomarker)
Pk_Malaysia_final_kable <- left_join(Pk_Malaysia_summary_wide, Pk_Malaysia_Bonferroni, by = c("Biomarker" = "name"))

original_biomarker_names <- c("APP", # In the same order as they are in Pk_Malaysia_final_kable
                              "Ang-1", 
                              "Ang-2", 
                              "Ang-2/Ang-1 ratio", 
                              "Aβ(1-42)", 
                              "BDNF", 
                              "BMP-9", 
                              "CCL18", 
                              "CCL2", 
                              "CCL4",
                              "CCL5",
                              "CNTN1",
                              "CRP",
                              "CaBD",
                              "Fetuin A",
                              "GFAP",
                              "GM-CSF",
                              "ICAM-1",
                              "IFNγ",
                              "IL-10",
                              "IL-17",
                              "IL-1RA",
                              "IL-1α",
                              "IL-1β",
                              "IL-2",
                              "IL-4",
                              "IL-6",
                              "IL-8",
                              "KLK6",
                              "Lipocalin 2",
                              "MIF",
                              "MPO",
                              "NCAM-1",
                              "NGF-β",
                              "NRGN",
                              "NSE",
                              "NfL",
                              "OPN",
                              "PDGF AA",
                              "PDGF BB",
                              "Park7",
                              "RAGE",
                              "S100B",
                              "Serpin E1",
                              "TDP-43",
                              "TNF-α",
                              "Tau total",
                              "UCHL-1",
                              "VCAM-1",
                              "VEGF",
                              "YKL40",
                              "vWF",
                              "αSyn")
Pk_Malaysia_final_kable$Biomarker <- original_biomarker_names

Pk_Malaysia_final_kable <- Pk_Malaysia_final_kable %>%
  kable(caption = "Biomarker median (pg/mL) and IQR, per group") %>%
  kable_styling(bootstrap_options = "striped", full_width = FALSE) %>%
      column_spec(column = 6, bold = Pk_Malaysia_final_kable$`P value` < 0.05) %>%
      column_spec(column = 7, bold = Pk_Malaysia_final_kable$`Corrected P value` < 0.05) 

Pk_Malaysia_final_kable
Biomarker median (pg/mL) and IQR, per group
Biomarker n CTRL n Pk Median (IQR) CTRL Median (IQR) Pk P value Corrected P value
APP 19 19 36081.91 ( 25944.92 ) 17700.68 ( 12728.15 ) 0.0009697 0.0513916
Ang-1 19 19 51806.54 ( 23225.52 ) 1547.848 ( 1633.809 ) 0.0000000 0.0000000
Ang-2 19 19 3187.404 ( 1276.748 ) 2561.727 ( 1365.137 ) 0.0470933 1.0000000
Ang-2/Ang-1 ratio 19 19 0.06218625 ( 0.0550491 ) 1.485368 ( 0.5784377 ) 0.0000000 0.0000000
Aβ(1-42) 19 19 0.2197 ( 1.15436 ) 1.587274 ( 0.8449642 ) 0.0769321 1.0000000
BDNF 19 19 1516.227 ( 1454.18 ) 146.203 ( 115.7521 ) 0.0000000 0.0000003
BMP-9 19 19 205.9015 ( 193.2082 ) 3.563467 ( 2.33453 ) 0.0000008 0.0000424
CCL18 19 19 76800.08 ( 37565.09 ) 76703.89 ( 73970.48 ) 0.8853133 1.0000000
CCL2 19 19 380.842 ( 325.9286 ) 160.88 ( 219.4045 ) 0.0075090 0.3979760
CCL4 19 19 781.81 ( 250.0735 ) 526.2747 ( 47.64977 ) 0.0000029 0.0001560
CCL5 19 19 158730.1 ( 96050.65 ) 17649.71 ( 27044.37 ) 0.0000000 0.0000000
CNTN1 19 19 18190.56 ( 4889.546 ) 6179.677 ( 2615.664 ) 0.0000000 0.0000000
CRP 19 19 668496.3 ( 308499 ) 477838.2 ( 235913.7 ) 0.0005433 0.0287954
CaBD 19 19 1807.297 ( 178.3879 ) 1151.089 ( 220.9344 ) 0.0000000 0.0000000
Fetuin A 19 19 176390803 ( 38746703 ) 154460943 ( 47209645 ) 0.0462397 1.0000000
GFAP 19 18 78.90895 ( 45.83352 ) 29.85289 ( 25.34285 ) 0.0000256 0.0013582
GM-CSF 19 19 23.77626 ( 10.0321 ) 21.52029 ( 8.598411 ) 0.2423497 1.0000000
ICAM-1 19 19 705781.7 ( 710687.9 ) 832536.7 ( 324396.6 ) 0.9769881 1.0000000
IFNγ 19 19 101.4179 ( 11.8365 ) 104.8003 ( 46.26716 ) 0.7253783 1.0000000
IL-10 19 19 7.995004 ( 1.332426 ) 48.62692 ( 115.9813 ) 0.0000001 0.0000076
IL-17 19 19 29.19267 ( 6.138356 ) 22.18039 ( 8.324175 ) 0.0080604 0.4272030
IL-1RA 19 19 6736.133 ( 2506.003 ) 35639.06 ( 21569.21 ) 0.0000000 0.0000000
IL-1α 19 19 49.0339 ( 46.40482 ) 46.03038 ( 21.17682 ) 0.2931215 1.0000000
IL-1β 19 19 24.66162 ( 7.188521 ) 40.03614 ( 17.9049 ) 0.0027363 0.1450245
IL-2 19 19 34.81916 ( 28.69849 ) 33.1313 ( 62.47699 ) 0.5291252 1.0000000
IL-4 19 19 165.066 ( 12.23509 ) 159.8323 ( 35.17321 ) 0.4383034 1.0000000
IL-6 19 18 11.48631 ( 2.631257 ) 21.7437 ( 19.21264 ) 0.0012495 0.0662253
IL-8 19 19 319.6725 ( 598.992 ) 254.5794 ( 2353.187 ) 0.8174633 1.0000000
KLK6 19 19 2319.536 ( 915.0541 ) 1072.432 ( 788.647 ) 0.0002475 0.0131159
Lipocalin 2 19 19 547607.5 ( 149670.6 ) 619695.2 ( 94134.2 ) 0.0496557 1.0000000
MIF 19 19 384.5975 ( 315.7701 ) 630.6708 ( 321.2088 ) 0.0022218 0.1177543
MPO 19 19 1571073 ( 869998.1 ) 3176104 ( 919811.7 ) 0.0006159 0.0326436
NCAM-1 19 19 107813.5 ( 38203.57 ) 70203.28 ( 8851.253 ) 0.0000016 0.0000833
NGF-β 17 19 0.9257768 ( 1.199232 ) 1.731491 ( 1.480931 ) 0.0203291 1.0000000
NRGN 18 19 22.3997 ( 11.08561 ) 241.3741 ( 763.047 ) 0.0000427 0.0022606
NSE 19 16 54323.95 ( 21601.19 ) 1094366 ( 1382298 ) 0.0009665 0.0512258
NfL 19 19 8.139866 ( 4.438695 ) 6.271222 ( 5.765971 ) 0.2120048 1.0000000
OPN 19 19 12980.99 ( 11051.35 ) 5056.864 ( 3291.073 ) 0.0033420 0.1771263
PDGF AA 19 19 3864.416 ( 688.8593 ) 552.6219 ( 346.1488 ) 0.0000000 0.0000000
PDGF BB 19 18 18494.51 ( 7645.818 ) 1385.736 ( 2055.839 ) 0.0000004 0.0000190
Park7 19 19 71436.38 ( 61371.81 ) 161810.2 ( 146171 ) 0.0000109 0.0005799
RAGE 19 19 7205.683 ( 2599.431 ) 4796.721 ( 1839.571 ) 0.0000495 0.0026236
S100B 19 19 27.3045 ( 0 ) 1282.191 ( 1777.194 ) 0.0000001 0.0000043
Serpin E1 18 19 932450 ( 468183.9 ) 183488.2 ( 103411 ) 0.0000000 0.0000000
TDP-43 17 19 5344.277 ( 5805.93 ) 67142.7 ( 90407.26 ) 0.0000971 0.0051444
TNF-α 19 19 16.22958 ( 5.688821 ) 13.71272 ( 2.301574 ) 0.0392982 1.0000000
Tau total 19 19 0.5577688 ( 0.57288 ) 3.685658 ( 4.909908 ) 0.0000131 0.0006926
UCHL-1 19 19 49.30495 ( 45.35367 ) 336.6019 ( 184.0623 ) 0.0000004 0.0000219
VCAM-1 19 19 698022.3 ( 419633.4 ) 1673430 ( 2154223 ) 0.0000024 0.0001261
VEGF 19 19 147.2947 ( 81.1504 ) 178.8963 ( 118.7221 ) 0.2548461 1.0000000
YKL40 19 19 29591.46 ( 24732.27 ) 58673.75 ( 33186.66 ) 0.0019910 0.1055228
vWF 19 19 6716.777 ( 8770.726 ) 6173.033 ( 3737.156 ) 0.2014334 1.0000000
αSyn 19 19 863.401 ( 259.8839 ) 2177.755 ( 392.3615 ) 0.0000004 0.0000219
Biomarkers of brain injury: Bonferroni-corrected group comparisons (right click to open full-size image in another tab/window)
Biomarkers of brain injury: Bonferroni-corrected group comparisons (right click to open full-size image in another tab/window)

Hierarchical clustering heatmaps

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Brain biomarker subset

Pk_Malaysia_final_brain_subset_list <- c(
  "αSyn", "APP", "Aβ(1-42)", "BDNF", "CaBD", "CNTN1", "NSE",
  "Fetuin A", "GFAP", "KLK6", "NCAM-1", "Lipocalin 2", "NGF-β", "NfL", "NRGN",
  "Park7", "S100B", "TDP-43", "Tau total", "UCHL-1", "YKL40"
)

Pk_Malaysia_final_brain_subset <- Pk_Malaysia_final %>% select(Participants, Pk_Malaysia_final_brain_subset_list)

row.names(Pk_Malaysia_final_brain_subset) <- Pk_Malaysia_final_brain_subset$Participants
Pk_Malaysia_final_brain_subset$Participants <- NULL

Pk_Malaysia_final_brain_subset[is.na(Pk_Malaysia_final_brain_subset)] <- 0 # Replace NAs with zeros
Pk_Malaysia_final_brain_subset <- scale(Pk_Malaysia_final_brain_subset) # Scale data 

Pk_Malaysia_final_brain_subset_dist <- dist(Pk_Malaysia_final_brain_subset) # Base R code
Pk_Malaysia_final_brain_subset_hclust <- hclust(Pk_Malaysia_final_brain_subset_dist)
plot(Pk_Malaysia_final_brain_subset_hclust)
pheatmap(Pk_Malaysia_final_brain_subset, cutree_rows = 2, cutree_cols = 2, scale = "none")
Biomarkers of brain injury: Hierarchical clustering heatmap (right click to open full-size image in another tab/window)
Biomarkers of brain injury: Hierarchical clustering heatmap (right click to open full-size image in another tab/window)

Hierarchical clustering heatmap analysis unveiled distinct group patterns for brain injury biomarkers, delineating cohesive clusters predominantly composed of infected individuals with elevated levels of S100B, Park7, αSyn, and TDP-43. Notably, two infected individuals displayed atypical clustering with the healthy controls, indicating a subgroup with a unique biomarker profile. Conversely, in the control group, certain biomarkers including BDNF, CaBD, CNTN1, and GFAP formed cohesive clusters with higher levels, representing a baseline biomarker profile in healthy individuals. However, two infected individuals clustered with the control group in this category, suggesting potential overlap or similarity in the levels of these specific biomarkers between infected and control individuals.

Immune biomarker subset

Pk_Malaysia_final_immune_subset_list <- c(
  "CRP", "GM-CSF", "IFNγ", "IL-1α", "IL-1β", "IL-2", "IL-6", "IL-8",
  "IL-17", "MIF", "MPO", "TNF-α", "IL-1RA", "IL-4", "IL-10",
  "CCL2", "CCL4", "CCL5", "CCL18", "OPN", "RAGE"
)

Pk_Malaysia_final_immune_subset <- Pk_Malaysia_final %>% select(Participants, Pk_Malaysia_final_immune_subset_list)

row.names(Pk_Malaysia_final_immune_subset) <- Pk_Malaysia_final_immune_subset$Participants
Pk_Malaysia_final_immune_subset$Participants <- NULL

Pk_Malaysia_final_immune_subset[is.na(Pk_Malaysia_final_immune_subset)] <- 0 # Replace NAs with zeros
Pk_Malaysia_final_immune_subset <- scale(Pk_Malaysia_final_immune_subset) # Scale data 

Pk_Malaysia_final_immune_subset_dist <- dist(Pk_Malaysia_final_immune_subset) # Base R code
Pk_Malaysia_final_immune_subset_hclust <- hclust(Pk_Malaysia_final_immune_subset_dist)
plot(Pk_Malaysia_final_immune_subset_hclust)
pheatmap(Pk_Malaysia_final_immune_subset, scale = "none")
Biomarkers of infection and immunity: Hierarchical clustering heatmap (right click to open full-size image in another tab/window)
Biomarkers of infection and immunity: Hierarchical clustering heatmap (right click to open full-size image in another tab/window)

Clustering analysis of biomarkers associated with infection and immune activation did not reveal distinct separation between the two groups. Higher levels of IL-1RA and MPO predominantly clustered in the infected group, with two control individuals exhibiting similar patterns. Additionally, one infected individual displayed notably high levels of IL-1β, GM-CSF, TNF-α, CCL4, and CCL2, while another exhibited elevated levels of OPN and IL-10. Furthermore, one control individual showed high levels of IL-6.

Vascular biomarker subset

Pk_Malaysia_final_vascular_subset_list <- c(
  "Ang-1", "Ang-2", "Ang-2/Ang-1 ratio", "BMP-9", "ICAM-1",
  "PDGF AA", "PDGF BB", "Serpin E1", "VCAM-1", "VEGF", "vWF"
)

Pk_Malaysia_final_vascular_subset <- Pk_Malaysia_final %>% select(Participants, Pk_Malaysia_final_vascular_subset_list)

row.names(Pk_Malaysia_final_vascular_subset) <- Pk_Malaysia_final_vascular_subset$Participants
Pk_Malaysia_final_vascular_subset$Participants <- NULL

Pk_Malaysia_final_vascular_subset[is.na(Pk_Malaysia_final_vascular_subset)] <- 0 # Replace NAs with zeros
Pk_Malaysia_final_vascular_subset <- scale(Pk_Malaysia_final_vascular_subset) # Scale data 

Pk_Malaysia_final_vascular_subset_dist <- dist(Pk_Malaysia_final_vascular_subset) # Base R code
Pk_Malaysia_final_vascular_subset_hclust <- hclust(Pk_Malaysia_final_vascular_subset_dist)
plot(Pk_Malaysia_final_vascular_subset_hclust)
pheatmap(Pk_Malaysia_final_vascular_subset, cutree_rows = 2, cutree_cols = 2,  scale = "none")
Vascular biomarkers: Hierarchical clustering heatmap (right click to open full-size image in another tab/window)
Vascular biomarkers: Hierarchical clustering heatmap (right click to open full-size image in another tab/window)

Lastly, clustering of vascular biomarkers exhibited clear and distinctive group profiles. In the control group, a cohesive cluster characterized by high levels of PDGF-AA, Ang-1, PDGF-BB, Serpin E1, and BMP-9 was observed. Conversely, in the infected group, a distinct cluster with higher levels of VCAM-1 and the Ang-2/Ang-1 ratio was identified.

Biomarker correlation with age

Pk_Malaysia_age <- Pk_Malaysia_plots %>%    
  ggplot(aes(y = Levels, x = age, color=status, group = sample_ID)) +
  geom_point(size = 1.5) + 
  geom_smooth(method = lm, formula = y ~ x, se = FALSE) + 
  xlab("Age") + ylab("pg/mL") +
  theme_classic() + 
  ggtitle("") + 
  facet_wrap(~Biomarker, scales = "free") +
  scale_color_manual(values=c("black", "red"), name = "Status") + 
  stat_cor(method = "spearman") + 
  geom_smooth(method = lm, formula = y ~ x, se = FALSE)

Pk_Malaysia_age
Biomarker correlation with age (right click to open full-size image in another tab/window)
Biomarker correlation with age (right click to open full-size image in another tab/window)

No significant correlations were identified between the levels of any of the examined biomarkers and the age of participants within each respective group.

Biomarker correlation with parasitaemia

Pk_Malaysia_par <- Pk_Malaysia_plots %>%    
  ggplot(aes(y = Levels, x = parasitaemia, color=status, group = sample_ID)) +
  geom_point(size = 1.5) + 
  geom_smooth(method = lm, formula = y ~ x, se = FALSE) + 
  xlab("Parasites/μL") + ylab("pg/mL") +
  theme_classic() + 
  ggtitle("") + 
  facet_wrap(~Biomarker, scales = "free") +
  scale_color_manual(values=c("black", "red"), name = "Status") + 
  stat_cor(method = "spearman") + 
  geom_smooth(method = lm, formula = y ~ x, se = FALSE) +
  scale_x_continuous(labels = scales::scientific_format(digits = 1, decimal.mark = ",", big.mark = "x10^")) +
  scale_y_continuous(labels = scales::scientific_format(digits = 1, decimal.mark = ",", big.mark = "x10^"))

Pk_Malaysia_par
Biomarker correlation with parasitaemia (right click to open full-size image in another tab/window)
Biomarker correlation with parasitaemia (right click to open full-size image in another tab/window)

Furthermore, within the Pk-infected group, no significant correlations were observed between biomarker levels and the percentage of parasitaemia.

Codebook

Go back to index

Pk_Malaysia_final_codebook <- describe(Pk_Malaysia_final[, -which(names(Pk_Malaysia_final) == "sample_ID")])
Pk_Malaysia_final_codebook
## Pk_Malaysia_final[, -which(names(Pk_Malaysia_final) == "sample_ID")] 
## 
##  62  Variables      38  Observations
## --------------------------------------------------------------------------------
## Participants 
##        n  missing distinct 
##       38        0       38 
## 
## lowest : Control 1     Control 10    Control 11    Control 12    Control 13   
## highest: Pk infected 5 Pk infected 6 Pk infected 7 Pk infected 8 Pk infected 9
## --------------------------------------------------------------------------------
## age 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       36        2       20    0.994     38.5    14.81     26.0     27.5 
##      .25      .50      .75      .90      .95 
##     29.0     32.0     44.5     61.0     66.5 
##                                                                             
## Value      24.00 25.65 26.75 27.85 28.95 29.50 30.60 31.70 32.80 35.00 37.75
## Frequency      1     2     1     3     3     2     5     4     3     1     1
## Proportion 0.028 0.056 0.028 0.083 0.083 0.056 0.139 0.111 0.083 0.028 0.028
##                                                                 
## Value      43.80 46.00 53.70 54.80 57.00 58.65 62.50 76.80 79.00
## Frequency      1     1     1     1     1     1     2     1     1
## Proportion 0.028 0.028 0.028 0.028 0.028 0.028 0.056 0.028 0.028
## 
## For the frequency table, variable is rounded to the nearest 0.55
## --------------------------------------------------------------------------------
## sex 
##        n  missing distinct    value 
##       38        0        1     Male 
##                
## Value      Male
## Frequency    38
## Proportion    1
## --------------------------------------------------------------------------------
## status 
##        n  missing distinct 
##       38        0        2 
##                                     
## Value           Control Pk infection
## Frequency            19           19
## Proportion          0.5          0.5
## --------------------------------------------------------------------------------
## parasitaemia 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       19       19       19        1    52402    82072      977     1168 
##      .25      .50      .75      .90      .95 
##     3728    15200    30474   123520   248366 
##                                                                       
## Value         410.00   9178.94  13563.41  17947.88  22332.35  26716.82
## Frequency          7         2         1         2         1         1
## Proportion     0.368     0.105     0.053     0.105     0.053     0.053
##                                                             
## Value       31101.29  39870.23  96868.34 224017.97 438857.00
## Frequency          1         1         1         1         1
## Proportion     0.053     0.053     0.053     0.053     0.053
## 
## For the frequency table, variable is rounded to the nearest 4384.47
## --------------------------------------------------------------------------------
## hospital 
##        n  missing distinct 
##       19       19        8 
## 
## lowest : Hospital Kota Tinggi                 Hospital Kuala Kubu Bharu            Hospital Kuala Lipis                 Hospital Mersing                     Hospital Setiu                      
## highest: Hospital Mersing                     Hospital Setiu                       Hospital Sungai Siput                Klinik Kesihatan Betau               Pejabat Kesihatan Daerah Kota Tinggi
## --------------------------------------------------------------------------------
## NAME_1 
##        n  missing distinct 
##       19       19        5 
##                                                             
## Value          Johor    Pahang     Perak  Selangor Trengganu
## Frequency          3         5         2         8         1
## Proportion     0.158     0.263     0.105     0.421     0.053
## --------------------------------------------------------------------------------
## latitude 
##        n  missing distinct     Info     Mean      Gmd 
##       19       19        8    0.917    3.741    1.057 
##                                                                          
## Value      1.738177 2.406427 3.546384 4.175325 4.253943 4.843576 5.669062
## Frequency         2        1        8        4        1        2        1
## Proportion    0.105    0.053    0.421    0.211    0.053    0.105    0.053
## 
## For the frequency table, variable is rounded to the nearest 0.03930885
## --------------------------------------------------------------------------------
## longitude 
##        n  missing distinct     Info     Mean      Gmd 
##       19       19        8    0.917    102.1   0.8879 
##                                                                          
## Value      101.0626 101.6319 101.6888 102.0304 102.7420 103.8237 103.8806
## Frequency         2        8        1        4        1        1        1
## Proportion    0.105    0.421    0.053    0.211    0.053    0.053    0.053
##                    
## Value      103.9091
## Frequency         1
## Proportion    0.053
## 
## For the frequency table, variable is rounded to the nearest 0.02846519
## --------------------------------------------------------------------------------
## S100B 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       24    0.939    798.8     1149    26.13    27.30 
##      .25      .50      .75      .90      .95 
##    27.30    97.44  1252.67  2596.85  2742.90 
##                                                                             
## Value          0    50   100   150   250   500   550  1050  1150  1250  1450
## Frequency     18     1     1     2     1     1     1     2     1     1     3
## Proportion 0.474 0.026 0.026 0.053 0.026 0.026 0.026 0.053 0.026 0.026 0.079
##                                         
## Value       2050  2550  2650  3000  5850
## Frequency      1     1     2     1     1
## Proportion 0.026 0.026 0.053 0.026 0.026
## 
## For the frequency table, variable is rounded to the nearest 50
## --------------------------------------------------------------------------------
## GFAP 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       37        1       37        1    73.99    65.14    16.93    19.41 
##      .25      .50      .75      .90      .95 
##    28.79    57.64    93.56   119.51   161.18 
## 
## lowest : 12.5061 14.8444 17.4455 17.637  20.5881
## highest: 119.118 120.086 137.167 257.212 398.535
## --------------------------------------------------------------------------------
## NfL 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    9.799    7.194    3.402    4.478 
##      .25      .50      .75      .90      .95 
##    5.185    7.431   10.872   20.797   24.833 
## 
## lowest : 1.97673 2.53153 3.55605 4.38567 4.51757
## highest: 20.7435 20.9222 24.4438 27.0413 37.6341
## --------------------------------------------------------------------------------
## Tau total 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    2.906    3.788   0.1591   0.2235 
##      .25      .50      .75      .90      .95 
##   0.5159   0.9164   3.6358   7.6384   8.4599 
## 
## lowest : 0.0909441 0.137882  0.162862  0.173781  0.244751 
## highest: 7.40206   8.18975   8.21202   9.86446   25.2736  
## --------------------------------------------------------------------------------
## UCHL-1 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    233.5    290.1    22.41    24.24 
##      .25      .50      .75      .90      .95 
##    42.40    98.94   328.25   408.91   459.46 
## 
## lowest : 3.83908 15.6483 23.6062 23.885  24.3992
## highest: 408.195 410.577 429.266 630.565 2533.03
## --------------------------------------------------------------------------------
## Park7 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1   135795   106138    28553    34804 
##      .25      .50      .75      .90      .95 
##    68699   127422   168596   314497   340531 
## 
## lowest : 21580.4 28094.9 28634.1 34677.9 34857.5
## highest: 308420  328676  339356  347189  349055 
## --------------------------------------------------------------------------------
## VCAM-1 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1  1406175  1288257   307337   348707 
##      .25      .50      .75      .90      .95 
##   558720  1017696  1603343  3799576  4341450 
## 
## lowest : 15554.9 295307  309460  327707  357707 
## highest: 3779070 3847420 4304260 4552220 4839010
## --------------------------------------------------------------------------------
## VEGF 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       37        1    183.9    116.3    82.35    88.41 
##      .25      .50      .75      .90      .95 
##   110.62   160.48   203.45   272.13   321.15 
## 
## lowest : 67.9363 76.8312 83.3222 85.4559 89.682 
## highest: 271.961 272.513 298.801 447.804 923.274
## --------------------------------------------------------------------------------
## αSyn 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1     1438    770.7    636.2    705.7 
##      .25      .50      .75      .90      .95 
##    840.6   1128.7   2154.7   2336.8   2379.2 
## 
## lowest : 562.292 626.417 637.871 680.323 716.527
## highest: 2334.73 2341.67 2362.51 2473.96 2483.27
## --------------------------------------------------------------------------------
## Ang-1 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    28284    32352     1066     1089 
##      .25      .50      .75      .90      .95 
##     1550    12721    51507    69674    78805 
## 
## lowest : 496.426 1013.14 1074.89 1085.18 1090.32
## highest: 66595.5 76855.7 77940.2 83703.7 84072  
## --------------------------------------------------------------------------------
## Ang-2 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       36        1     3807     2726     1687     1776 
##      .25      .50      .75      .90      .95 
##     2142     2999     3615     6883     9338 
## 
## lowest : 559.12  1408.85 1735.61 1793.46 1829.59
## highest: 6778.36 7126.48 9183.24 10212.2 17109.1
## --------------------------------------------------------------------------------
## APP 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       37        1    30928    24085     8244    10082 
##      .25      .50      .75      .90      .95 
##    13634    23428    37387    53867    85948 
## 
## lowest : 7885.4  8153.22 8259.46 8679.6  10682.7
## highest: 53796.7 54030.8 82132.9 107566  111355 
## --------------------------------------------------------------------------------
## BMP-9 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       34    0.999    113.8    144.6    2.314    2.886 
##      .25      .50      .75      .90      .95 
##    3.563   37.683  204.781  333.845  365.306 
## 
## lowest : 1.48794 2.12933 2.34642 3.11682 3.22808
## highest: 327.321 349.067 362.144 383.222 419.644
## --------------------------------------------------------------------------------
## CaBD 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1     1453    444.1    871.0    971.4 
##      .25      .50      .75      .90      .95 
##   1156.1   1429.1   1802.9   1907.1   2018.1 
## 
## lowest : 770.303 860.826 872.843 942.805 983.6  
## highest: 1890.42 1945.9  2008.5  2072.43 2120.95
## --------------------------------------------------------------------------------
## CCL2 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    674.7    885.4    67.89    93.50 
##      .25      .50      .75      .90      .95 
##   132.34   293.69   562.38  1156.60  2096.66 
## 
## lowest : 56.919  60.936  69.1141 91.6015 94.3202
## highest: 1092.28 1306.68 2062.36 2291.03 8463.64
## --------------------------------------------------------------------------------
## CCL4 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       30    0.998     1169     1217    433.0    492.4 
##      .25      .50      .75      .90      .95 
##    527.5    572.4    787.5    907.1   1459.8 
##                                                                             
## Value        400   450   500   550   600   700   750   800   850   900  1100
## Frequency      3     2     9     7     3     2     3     1     3     2     1
## Proportion 0.079 0.053 0.237 0.184 0.079 0.053 0.079 0.026 0.079 0.053 0.026
##                       
## Value       3450 17950
## Frequency      1     1
## Proportion 0.026 0.026
## 
## For the frequency table, variable is rounded to the nearest 50
## --------------------------------------------------------------------------------
## CNTN1 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    12301     8370     2393     3398 
##      .25      .50      .75      .90      .95 
##     6202    13723    18031    21212    22175 
## 
## lowest : 110.522 240.652 2772.58 3001.67 3567.66
## highest: 20982.7 21747.5 21790.2 24357.7 28555.5
## --------------------------------------------------------------------------------
## GM-CSF 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       26    0.998    58.43    78.01    14.05    15.61 
##      .25      .50      .75      .90      .95 
##    17.99    23.40    27.39    31.66    38.91 
## 
## lowest : 10.6    13.443  14.1627 15.6117 16.3408
## highest: 31.4273 32.2023 38.8542 39.2488 1371.21
## --------------------------------------------------------------------------------
## ICAM-1 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1   834610   421642   325324   428277 
##      .25      .50      .75      .90      .95 
##   571495   827288  1116669  1342593  1395878 
## 
## lowest : 72927.6 256136  337534  370980  452833 
## highest: 1331940 1367440 1393970 1406700 1566610
## --------------------------------------------------------------------------------
## IFNγ 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       23    0.995    122.3    52.82    83.85    86.78 
##      .25      .50      .75      .90      .95 
##    95.78   101.98   110.16   147.29   208.88 
## 
## 71.0418353109784 (1, 0.026), 79.2535118340771 (1, 0.026), 83.3593500956264 (4,
## 0.105), 87.4651883571757 (1, 0.026), 91.5710266187251 (2, 0.053),
## 95.6768648802744 (6, 0.158), 99.7827031418238 (6, 0.158), 103.888541403373 (4,
## 0.105), 107.994379664922 (4, 0.105), 112.100217926472 (2, 0.053),
## 120.31189444957 (1, 0.026), 124.41773271112 (1, 0.026), 140.841085757317 (1,
## 0.026), 153.158600541965 (1, 0.026), 173.687791849712 (1, 0.026),
## 399.508896234925 (1, 0.026), 481.625661465912 (1, 0.026)
## 
## For the frequency table, variable is rounded to the nearest 4.105838
## --------------------------------------------------------------------------------
## IL-10 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       30    0.998    53.07     75.4    6.354    6.848 
##      .25      .50      .75      .90      .95 
##    7.995    8.660   48.181  153.730  190.433 
##                                                                             
## Value          4     6     8    10    12    16    26    28    44    46    48
## Frequency      1    12     8     1     1     1     1     1     1     1     1
## Proportion 0.026 0.316 0.211 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026
##                                                                 
## Value         50    78    84   126   148   164   178   254   524
## Frequency      1     1     1     1     1     1     1     1     1
## Proportion 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026
## 
## For the frequency table, variable is rounded to the nearest 2
## --------------------------------------------------------------------------------
## IL-17 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       21    0.994    27.51    8.924    16.79    18.41 
##      .25      .50      .75      .90      .95 
##    22.18    26.56    32.26    36.47    39.98 
## 
## lowest : 15.1749 16.0501 16.9255 17.801  18.6766
## highest: 36.2101 37.0875 39.7203 41.4758 55.5272
## --------------------------------------------------------------------------------
## IL-1α 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       35    0.999    53.22    25.91    27.45    31.10 
##      .25      .50      .75      .90      .95 
##    35.96    47.16    66.84    89.82    96.11 
## 
## lowest : 15.0505 25.2321 27.8437 29.4365 31.807 
## highest: 89.4051 90.804  95.335  100.535 102.09 
## --------------------------------------------------------------------------------
## IL-2 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       24    0.995    52.41    45.63    12.13    17.44 
##      .25      .50      .75      .90      .95 
##    23.22    34.82    64.37   134.33   146.58 
## 
## lowest : 7.82701 12.8851 16.2581 17.9449 21.3189
## highest: 132.804 137.876 146.33  148.02  170.004
## --------------------------------------------------------------------------------
## IL-4 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       25    0.996    167.4    26.92    130.4    142.9 
##      .25      .50      .75      .90      .95 
##    156.2    165.1    177.3    191.6    228.5 
## 
## lowest : 115.203 118.168 132.597 140.973 143.723
## highest: 185.418 187.902 200.147 228.477 234.214
## --------------------------------------------------------------------------------
## IL-6 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       37        1       26    0.996    41.72    55.42    10.38    10.54 
##      .25      .50      .75      .90      .95 
##    11.49    13.15    27.02    61.26    71.50 
## 
## lowest : 8.718   10.3787 10.6556 10.9325 11.4863
## highest: 57.5892 66.7718 70.3901 75.9579 758.135
## --------------------------------------------------------------------------------
## IL-8 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1     1219     1690    29.46    56.00 
##      .25      .50      .75      .90      .95 
##   187.19   305.34   969.25  4078.44  5213.84 
## 
## lowest : 16.2134 16.4467 31.7564 34.4507 65.2314
## highest: 3837.39 4640.89 5131.2  5682.16 6480.9 
## --------------------------------------------------------------------------------
## IL-1β 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       28    0.998    208.5    361.5    21.42    21.94 
##      .25      .50      .75      .90      .95 
##    23.63    29.79    45.15    66.76   124.91 
## 
## lowest : 17.4576 20.548  21.5771 22.0915 22.6057
## highest: 59.4334 83.8591 121.415 144.722 6494.61
## --------------------------------------------------------------------------------
## OPN 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       36        1    29434    44957     3803     4276 
##      .25      .50      .75      .90      .95 
##     5025     7866    15264    24995    60000 
## 
## lowest : 2410.69 3235.79 3902.7  4086.45 4357.41
## highest: 23111.9 29389.9 57787   72540.9 650771 
## --------------------------------------------------------------------------------
## PDGF AA 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1     2323     1974    194.8    395.6 
##      .25      .50      .75      .90      .95 
##    580.9   1790.7   3862.7   4281.8   4660.9 
## 
## lowest : 185.197 194.709 194.841 390.951 397.619
## highest: 4194.95 4484.38 4549.26 5293.59 5416.15
## --------------------------------------------------------------------------------
## RAGE 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1     5832     2160     3449     3627 
##      .25      .50      .75      .90      .95 
##     4609     5517     7194     8432     8995 
## 
## lowest : 2001.02 3032.51 3522.62 3544.69 3662.61
## highest: 8232.88 8897.76 8977.23 9096.55 9639.36
## --------------------------------------------------------------------------------
## TNF-α 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       26    0.997    34.09    40.65    11.03    12.04 
##      .25      .50      .75      .90      .95 
##    13.29    14.55    17.92    23.35    46.37 
##                                                                             
## Value        9.5  11.0  12.0  12.5  13.0  13.5  14.0  14.5  15.0  16.0  17.5
## Frequency      2     1     3     3     2     5     1     5     2     2     3
## Proportion 0.053 0.026 0.079 0.079 0.053 0.132 0.026 0.132 0.053 0.053 0.079
##                                                           
## Value       19.0  19.5  20.0  22.5  25.0  42.0  68.5 652.0
## Frequency      1     1     2     1     1     1     1     1
## Proportion 0.026 0.026 0.053 0.026 0.026 0.026 0.026 0.026
## 
## For the frequency table, variable is rounded to the nearest 0.5
## --------------------------------------------------------------------------------
## vWF 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1     7748     5670     1838     2904 
##      .25      .50      .75      .90      .95 
##     3952     6364    11388    16290    17664 
## 
## lowest : 603.296 687.787 2040.7  2782.31 2955.45
## highest: 16258   16364   17652.7 17730.6 18525.5
## --------------------------------------------------------------------------------
## Aβ(1-42) 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       17    0.948    2.252    3.136   0.2197   0.2197 
##      .25      .50      .75      .90      .95 
##   0.2197   1.1581   1.9164   3.3664   5.8548 
##                                                                             
## Value       0.1933680  0.5168277  0.8402874  1.1637471  1.4872069  1.8106666
## Frequency          15          1          3          2          7          2
## Proportion      0.395      0.026      0.079      0.053      0.184      0.053
##                                                                             
## Value       2.1341263  3.1045055  3.4279652  5.0452638  9.5736998 32.5393399
## Frequency           3          1          1          1          1          1
## Proportion      0.079      0.026      0.026      0.026      0.026      0.026
## 
## For the frequency table, variable is rounded to the nearest 0.3234597
## --------------------------------------------------------------------------------
## BDNF 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1     1075     1391    57.73    82.52 
##      .25      .50      .75      .90      .95 
##   146.28   573.20  1475.78  2681.79  3978.30 
## 
## lowest : 2.3164  25.7357 63.371  70.8742 87.5161
## highest: 2376.89 3393.21 3673.41 5706.02 6223.19
## --------------------------------------------------------------------------------
## KLK6 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1     1719     1120    485.7    600.2 
##      .25      .50      .75      .90      .95 
##    873.5   1588.6   2345.4   2956.8   3245.9 
## 
## lowest : 36.9038 217.137 533.14  540.893 625.628
## highest: 2904.88 3077.92 3161.85 3722.45 3771.81
## --------------------------------------------------------------------------------
## MIF 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    545.9    326.4    133.6    154.1 
##      .25      .50      .75      .90      .95 
##    326.7    576.3    713.9    924.6   1010.1 
## 
## lowest : 98.2922 114.485 137.001 150.099 155.794
## highest: 905.818 968.269 996.884 1085.15 1175.51
## --------------------------------------------------------------------------------
## NCAM-1 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    88282    33847    48803    61094 
##      .25      .50      .75      .90      .95 
##    70018    82791   107151   129577   140988 
## 
## lowest : 9793.05 41135.8 50156.2 57708.7 62544.7
## highest: 129056  130793  140808  142005  155239 
## --------------------------------------------------------------------------------
## NGF-β 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       36        2       23    0.994     2.42    2.608   0.3394   0.4358 
##      .25      .50      .75      .90      .95 
##   0.9258   1.5288   2.5528   5.6670   8.7608 
## 
## lowest : 0.151001 0.339407 0.532259 0.62981  0.727961
## highest: 4.01429  7.31981  7.6434   12.1129  12.8833 
## --------------------------------------------------------------------------------
## NRGN 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       37        1       28    0.997    388.3    608.8     6.69    15.42 
##      .25      .50      .75      .90      .95 
##    24.42    43.73   385.17  1094.99  1961.83 
##                                                                             
## Value          0    20    40    60    80   120   200   240   380   460   560
## Frequency      5    13     4     1     1     1     1     1     1     1     1
## Proportion 0.135 0.351 0.108 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027
##                                               
## Value        800   980  1240  1780  2620  3280
## Frequency      2     1     1     1     1     1
## Proportion 0.054 0.027 0.027 0.027 0.027 0.027
## 
## For the frequency table, variable is rounded to the nearest 20
## --------------------------------------------------------------------------------
## TDP-43 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       36        2       35        1    41943    54839     1224     1701 
##      .25      .50      .75      .90      .95 
##     4843     8603    67199   126596   147669 
## 
## 0 (6, 0.167), 2000 (3, 0.083), 4000 (2, 0.056), 6000 (7, 0.194), 8000 (1,
## 0.028), 14000 (1, 0.028), 24000 (1, 0.028), 30000 (1, 0.028), 36000 (1, 0.028),
## 44000 (1, 0.028), 48000 (2, 0.056), 66000 (2, 0.056), 68000 (1, 0.028), 72000
## (1, 0.028), 120000 (1, 0.028), 122000 (1, 0.028), 130000 (1, 0.028), 142000 (1,
## 0.028), 158000 (1, 0.028), 214000 (1, 0.028)
## 
## For the frequency table, variable is rounded to the nearest 2000
## --------------------------------------------------------------------------------
## YKL40 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    46817    30652    15947    17134 
##      .25      .50      .75      .90      .95 
##    26161    41722    60201    72943    85597 
## 
## lowest : 10188.7 14008.1 16289.4 16461.4 17422.5
## highest: 71935.7 75294   80955.9 111898  159529 
## --------------------------------------------------------------------------------
## Ang-2/Ang-1 ratio 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1   0.9781     1.28  0.03617  0.04194 
##      .25      .50      .75      .90      .95 
##  0.06606  0.49856  1.48002  1.87343  2.44431 
##                                                                             
## Value       0.00  0.05  0.10  0.45  0.55  0.60  0.95  1.10  1.15  1.20  1.30
## Frequency      7     7     4     2     1     1     1     1     1     1     1
## Proportion 0.184 0.184 0.105 0.053 0.026 0.026 0.026 0.026 0.026 0.026 0.026
##                                                           
## Value       1.45  1.55  1.65  1.70  2.15  2.30  2.95  9.05
## Frequency      2     1     1     3     1     1     1     1
## Proportion 0.053 0.026 0.026 0.079 0.026 0.026 0.026 0.026
## 
## For the frequency table, variable is rounded to the nearest 0.05
## --------------------------------------------------------------------------------
## CCL18 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    81588    62176     8149    26083 
##      .25      .50      .75      .90      .95 
##    47290    76752    95442   112587   164770 
## 
## lowest : 763.221 6996.59 8351.88 22232.5 27733.3
## highest: 110399  117692  152000  237135  418536 
## --------------------------------------------------------------------------------
## CRP 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1   669006   423672   210001   272911 
##      .25      .50      .75      .90      .95 
##   446637   576268   747808   936719  1224757 
## 
## lowest : 86142   187534  213966  262556  277348 
## highest: 895015  1034030 1114170 1851430 2922320
## --------------------------------------------------------------------------------
## NSE 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       35        3       25    0.997  1439805  2523330    38473    45664 
##      .25      .50      .75      .90      .95 
##    50069    72502   954236  1638248  4854966 
## 
## 0 (9, 0.257), 50000 (12, 0.343), 150000 (2, 0.057), 250000 (1, 0.029), 8e+05
## (1, 0.029), 850000 (1, 0.029), 1e+06 (1, 0.029), 1100000 (1, 0.029), 1150000
## (1, 0.029), 1500000 (1, 0.029), 1550000 (1, 0.029), 1650000 (1, 0.029), 4250000
## (1, 0.029), 6150000 (1, 0.029), 28300000 (1, 0.029)
## 
## For the frequency table, variable is rounded to the nearest 50000
## --------------------------------------------------------------------------------
## PDGF BB 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       37        1       36        1    10313    10404    580.5    676.3 
##      .25      .50      .75      .90      .95 
##   1405.5   9639.1  18494.5  21719.8  25186.0 
## 
## lowest : 179.236 490.813 602.886 725.233 1006.73
## highest: 20624.9 23362.3 24671.4 27244.4 31458.5
## --------------------------------------------------------------------------------
## Fetuin A 
##         n   missing  distinct      Info      Mean       Gmd       .05       .10 
##        38         0        38         1 167921152  36295598 110957073 126800493 
##       .25       .50       .75       .90       .95 
## 150126663 168497529 189420188 204618271 219218301 
## 
## lowest : 93561700  110710000 111001000 121345000 129139000
## highest: 202523000 209508000 218996000 220477000 225856000
## --------------------------------------------------------------------------------
## CCL5 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1   105115   106540     8071     8860 
##      .25      .50      .75      .90      .95 
##    17678    89336   158612   233731   270829 
## 
## lowest : 4593.98 6928.17 8273.21 8465.71 9028.83
## highest: 229064  244620  261910  321370  340870 
## --------------------------------------------------------------------------------
## IL-1RA 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1    27955    27705     5007     5225 
##      .25      .50      .75      .90      .95 
##     6772    24643    35599    53814    62624 
## 
## lowest : 3566.67 4686.05 5063.52 5158.19 5252.98
## highest: 53513.6 54514.2 55237.5 104478  146835 
## --------------------------------------------------------------------------------
## MPO 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1  2517185  1380762   760080   853461 
##      .25      .50      .75      .90      .95 
##  1536309  2553248  3339946  4093459  4257109 
## 
## lowest : 563427  740996  763448  767231  890417 
## highest: 4078600 4128130 4240650 4350370 5432490
## --------------------------------------------------------------------------------
## Lipocalin 2 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       38        0       38        1   560971   152631   311377   357476 
##      .25      .50      .75      .90      .95 
##   493524   600060   659188   680091   717739 
## 
## lowest : 259243 291820 314829 346531 362167, highest: 679402 681700 705004 789906 858654
## --------------------------------------------------------------------------------
## Serpin E1 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       37        1       37        1   593106   568500    61600   109102 
##      .25      .50      .75      .90      .95 
##   183488   347682   930071  1293283  1389225 
## 
## lowest : 44724.3 54937.8 63265.2 90599   121437 
## highest: 1268240 1330850 1365640 1483550 2409590
## --------------------------------------------------------------------------------